Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus
Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus
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DOI：
10.1073/pnas.1320001111
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发表时间：
2014-03-18
影响因子：
11.1
通讯作者：
Schneider, Gisbert
中科院分区：
文献类型：
--
作者：
Reker, Daniel;Rodrigues, Tiago;Schneider, Gisbert
从头开始的分子设计和多药理学谱的计算机预测是新兴的研究课题，将深刻地影响药物发现和化学生物学的未来。目的是确定新的化学制剂的大分子靶标。虽然有几种预测这些目标的计算工具是公开可用的，但这些方法都没有明确设计用于预测新设计分子的目标接合。在这里，我们展示了一种独特技术的发展和实际应用，即基于自组织图谱的药物等效关系预测（SPiDER），它融合了自组织图谱、共识评分和统计分析的概念，成功地确定了已知药物和计算机生成的分子支架的靶标。我们发现了非诺贝特相关化合物的潜在脱靶性，并且在全面的前瞻性应用中，我们确定了从头设计的分子的多靶标调节谱。这些结果表明，在化学生物学和药物发现的早期阶段，SPiDER可用于识别创新化合物，并有助于研究药物的潜在副作用及其再利用选择。
De novo molecular design and in silico prediction of polypharmacological profiles are emerging research topics that will profoundly affect the future of drug discovery and chemical biology. The goal is to identify the macromolecular targets of new chemical agents. Although several computational tools for predicting such targets are publicly available, none of these methods was explicitly designed to predict target engagement by de novo-designed molecules. Here we present the development and practical application of a unique technique, self-organizing map-based prediction of drug equivalence relationships (SPiDER), that merges the concepts of self-organizing maps, consensus scoring, and statistical analysis to successfully identify targets for both known drugs and computer-generated molecular scaffolds. We discovered a potential off-target liability of fenofibrate-related compounds, and in a comprehensive prospective application, we identified a multitarget-modulating profile of de novo designed molecules. These results demonstrate that SPiDER may be used to identify innovative compounds in chemical biology and in the early stages of drug discovery, and help investigate the potential side effects of drugs and their repurposing options.